Fault diagnosis is a process in which various checking and testing methods are used to determine a status and an abnormal situation of a system and locate a type of a fault or a cause why a fault is generated, and finally, a solution is provided to perform fault recovery.
Fault diagnosis is an important process in many industrial systems. In recent years, as the modern industry demonstrates a trend of becoming larger and more complex, fault diagnosis becomes more important, and a greater challenge is imposed on a fault diagnosis technology.
Conventional fault diagnosis manners mainly include two types: one type is to establish an accurate fault diagnosis model, and the other type is to perform diagnosis based on experience of experts. The foregoing two fault diagnosis modes are mainly applicable to fault diagnosis of a simple system. However, data of a big-data network system (such as a network of a telecommunications operator or a large data center network) is of various types, and includes structured data, such as data in a database, and also includes non-structured data, such as a graph or text. In addition, the big-data network system generates massive data each day, and network system fault symptoms and causes are also diversified. Therefore, it is very difficult to perform diagnosis by relying on a conventional fault diagnosis method.